The way Starbucks has used data and modern technology to gain a competitive advantage is instructive for all businesses, big and small. For example, it was a pioneer in combining membership systems, payment cards and mobile apps. (But that’s just the surface)
This article will focus on how Starbucks is leveraging data, ARTIFICIAL intelligence and the Internet of Things to achieve competitive advantage. We’ll show you five interesting examples. Some argue that Starbucks may have become more of a data technology company in food and beverage than a pure coffee business.
How does Starbucks view the direct relationship between data, technology and business?
Starbucks doesn’t have a bad data base. It has more than 30,000 stores worldwide and makes nearly 100 million transactions a week. This gives them a comprehensive understanding of their customers’ spending habits and preferences. What’s surprising is that it hasn’t been until the last decade that Starbucks has really taken this data seriously.
It’s not that they haven’t thought about using the data before. But, as with most major corporate changes, it was the crisis that led to the change. In 2008, the outbreak of the financial crisis, many related stores have been closed. The lesson of the crisis for Howard Schultz, the company’s chief executive at the time, was that more analysis of Starbucks’s data was needed, especially when it came to location.
Until then, Starbucks’ decisions, like those of many other organizations, were based on experience and judgment, and were people-centered. Data is obviously important, but it’s not very systematic. But there were few articles on the subject at the time, and more on traditional methods of using data to validate and inform human ideas and decisions.
What Starbucks has done particularly well is to use data and technology to test out new ideas, and then use more data to determine which ideas can be advanced.
In addition to store locations, Starbucks’ use of data now extends, of course, to a range of marketing and product activities. This, in turn, has led to wisdom about how to manage their supply chains. One of the centerpieces is Starbucks’ Starbucks Rewards Loyalty program, which also began in 2008.
Less common is starbucks’ use of data, including the Internet of Things — particularly its in-store business. From the management of coffee machines to the optimization of other in-store equipment systems (such as ovens).
How does Starbucks use data? Five examples of ai + iot gaining competitive advantage
Of the many great examples of how Starbucks is using data and related technologies to gain a competitive advantage, I’ve chosen five highlights. These were chosen because they show how data and technologies like ARTIFICIAL intelligence, the Internet of Things and the cloud can be used to improve Starbucks’ business: targeting customers with personalized promotions and offers
- Insight-driven product development, including across channels
- Complex store location planning
- Dynamic menu creation and adjustment
- Optimization of machine maintenance
Example 1: Personalized promotions
The classic use of customer data is to personalize your service and offer based on individual consumer preferences, and Starbucks is no exception. Starbucks has more than 16 million members in the United States alone, and its “loyalty program” accounts for nearly half of all store transactions in the country.
Understanding individual customers’ order preferences and buying patterns enables Starbucks to deliver more targeted, personalized offers. Using AI to determine such activities is becoming a standard application of AI, something Starbucks has been doing since 2017 and followed up with its Digital Flywheel program.
One focus of the program is to recommend new products consumers might like, based on other products they have ordered.
But it’s not just about personalized promotions. A lot of it is still traditional mass marketing, but it’s different in that it goes directly to every consumer in the target market segment. These might include cold drinks on a hot day, product launches or seasonal menu launches.
Example 2: Insight-driven products
There is no doubt that personalized promotions work very well for Starbucks. But just as important for Starbucks is using customer buying data to develop its product range.
Starbucks has also done an excellent job of using data on the buying habits of large numbers of consumers. From these data, we can see the changes and development of existing products. For example, there was a cute idea 15 years ago to introduce pumpkin flavored drinks for Halloween (which has become a global model for pumpkin-inspired products). And this move, also let starbucks fall customer traffic surge.
The second type is using data across channels. The most important example is Starbucks’ entry into the home coffee business in 2016. Supermarkets are introducing home coffee to their customers. In-store data gives it a strong basis for deciding which products are suitable for home coffee drinkers. It can even test take-home generic store products like instant coffee.
On the other hand, it also tested whether to add sugar or milk to the family coffee.
Example 3: Store location planning
It’s a very complicated data analysis of where to plan starbucks stores. Starbucks is using data in just about every way you can think of.
The AI system of the store location enables the simulation of the business situation of the location. These include the region’s population, income levels, traffic, competitors and so on. At the same time, it can also help it to predict the possible revenue, profit and economic performance. The system also takes into account the location of existing Starbucks stores and the impact of proposed new locations on revenue at existing stores in nearby areas.
The AI technology at the heart of this application is location-based analytics, also known as GIS (Geographic Information System).
Example 4: Dynamic menu
One implication of the above example is starbucks’ ability to constantly refine and adapt its products. The way Starbucks uses data means it can tailor a company’s sales strategy to customers, locations and times. This affects products, promotions and pricing.
However, if you display your in-store items on the category board above the counter, you won’t be able to constantly adjust the items. That’s one reason inefficient solutions like blackboards are still popular with retailers. But for Starbucks, the solution is digital signage in stores, with menus set by computers.
This allows a complete chain to be formed, customizing strategies to enhance the customer experience to different local characteristics, while also being presented directly in the store.
Of course, there are some problems, such as too many stores that need to customize products and menus, which can make the process cumbersome. Even so, it’s still a system strategy that Starbucks will focus on. In 2018, Starbucks has been experimenting with a handful of stores, focusing on specific products based on conditions such as local weather or time of day.
Example 5: Optimizing the machine maintenance process
The final example is the maintenance of coffee machines and general in-store machinery.
Typical Starbucks stores have low transaction costs, short duration and high turnover rate. High customer throughput is the key to the success of offline stores. So if one machine fails, it can seriously affect the shop business.
Starbucks does not send engineers into stores to repair machines when they break down. Instead, they send the faulty machine to a maintenance engineer for technical maintenance. This can greatly improve the maintenance efficiency of the machine, and will not affect the shop business.
Machine failures occur from time to time, and sending machines back for repair also means that data can be collected on machine failures, machine use and repair methods. This routine data collection and regular analysis can help detect patterns or trends. AI can help predict failures and repair needs through data.
As a result, Starbucks has developed a new coffee machine, Clover X, which is currently only available in its flagship and concept stores. Not only is this coffee machine great in its ability to brew coffee, but it also connects to the cloud. The idea is that Starbucks will be able to diagnose and even repair machines remotely, as well as collect more comprehensive operational data.
Similar concepts apply to other machines. For example, the store now has a standard oven, also computer-controlled, for constant heating of products. However, computers currently need to be updated via USB drives, which happens whenever the machine configuration changes (for example, new on a product). But in the future, machines will be plugged directly into the cloud, relying more on AI for updates and content changes.
Starbucks is a very good example of how to lead modern global business. How it uses data is an example of how data management and technology can make a big difference. There’s nothing surprising about Starbucks’ use of data and ARTIFICIAL intelligence, and there’s nothing surprising about innovation in ARTIFICIAL intelligence or analytics.
But the way Starbucks uses data is a textbook example of how to start using data strategically, systematically and thoroughly.
Artificial intelligence, on the other hand, seems to be part of Starbucks’ journey to learn how to use data. This is not something that happens because of a strong desire to use AI, it is a choice made at the right time and the necessary point in different areas.
Another takeaway from discussing the Starbucks case is the way it extends the solution. In general, once a concept is proven, it means more than just making the concept bigger, because the globalization of the business increases the complexity of the regional segment. Further analysis is needed.
Most of us don’t compare our organization to Starbucks and don’t think we have much in common. But if we zoom in to the way Starbucks uses its data, things change. Seeing how it really applies ai to business development makes today’s discussion meaningful.
Like Starbucks, most of us don’t see ourselves in the AI or data business. But that doesn’t mean they won’t be central to our organization. It does make you question what kind of business you’re in — is it just your hottest product, or what you’re best at?
Original link: www.aiprescience.com/?s=Starbuck…
Welcome to”Jingdong cloud”Learn more.
The above information comes from the network, edited by the “jingdong Cloud developer community” public number, does not represent the position of jingdong cloud.
